AI-Assisted NDI & Defect Recognition Essentials Training by Tonex
Modern inspection teams are turning to AI to accelerate Non-Destructive Inspection (NDI) without compromising rigor. This course grounds learners in core NDI methods and shows how computer vision and machine learning raise defect detection accuracy, speed, and auditability. You will learn how to select sensors, shape data pipelines, and govern models for reliable outcomes. Because inspection data increasingly touches connected plants and defense supply chains, we address risks from compromised models and tampered datasets. You will also see how secure-by-design workflows protect inspection integrity, minimize exposure to adversarial inputs, and safeguard decision trails for compliance and incident review.
Learning Objectives:
- Explain foundational NDI methods and their practical strengths and limits
- Design data pipelines for image, signal, and metadata capture at scale
- Apply ML/CV techniques to classify, localize, and quantify defects
- Validate and monitor models for bias, drift, and operational robustness
- Embed secure controls so inspection, models, and results support cybersecurity requirements
Audience:
- Quality and Reliability Engineers
- Manufacturing and Maintenance Leaders
- AI/ML and Data Engineers
- NDI/NDE Technicians and Supervisors
- Operations and Plant Managers
- Cybersecurity Professionals
Course Modules:
Module 1: NDI Fundamentals
- Visual, ultrasonic, eddy current, radiographic overview
- Signal and image basics for inspection data
- Probability of detection and false alarm tradeoffs
- Calibration, reference standards, and traceability
- Data labeling discipline and inter-rater agreement
- Safety, ethics, and regulated environments
Module 2: Sensors, Data, And MLOps
- Modality selection and fixture design
- Acquisition protocols and sampling strategies
- Data schemas, metadata, and lineage capture
- Preprocessing, augmentation, and normalization
- Storage tiers, retention, and retrieval patterns
- MLOps for ingestion, versioning, and deploy
Module 3: Computer Vision For NDI
- Classical CV filters, morphology, segmentation
- CNN backbones, detection, and instance masks
- Transformers and attention for defect cues
- Multimodal fusion of image and signal data
- Weak supervision and active learning loops
- Latency, throughput, and on-edge constraints
Module 4: Model Validation And QA
- Test design, cross-validation, and holdouts
- ROC, PR curves, and cost-sensitive metrics
- Uncertainty quantification and thresholds
- Robustness to noise, blur, and shift
- Human-in-the-loop review and escalation
- Documentation, change control, and audits
Module 5: Secure And Trusted Inspection
- Threats to sensors, models, and datasets
- Access control, encryption, and key handling
- Model signing, provenance, and SBOM records
- Adversarial input screening and sanity checks
- Secure deployment at edge and cloud
- Compliance mapping and evidence capture
Module 6: Operations, ROI, And Scaling
- Workflow design and takt time alignment
- Change management and operator enablement
- Cost models, savings, and yield impact
- Continuous monitoring and alerting playbooks
- Continuous improvement with feedback data
- Roadmap for phased enterprise rollout
Elevate inspection accuracy, accelerate throughput, and harden your quality pipeline with AI you can trust. Enroll now to build an end-to-end NDI capability that is measurable, secure, and production-ready.